OPTIMIZING AND ENHANCING PARALLEL MULTI STORAGE BACKUP COMPRESSION FOR REAL-TIME DATABASE SYSTEMS

One of the big challenges in the world was the amount of data being stored, especially in Data Warehouses. Data stored in databases keep growing as a result of businesses requirements for more information. A big portion of the cost of keeping large amounts of data is in the cost of disk systems, and the resources utilized in managing the data. Backup Compression field in the database systems has tremendously revolutionized in the past few decades. Most existing work presented attribute-level compression methods but the main contribution of such work had been on the numerical attributes compression alone, in which it employed to reduce the length of integers, dates and floating point numbers. Nevertheless, the existing work has not evolved the process of compressing string-valued attributes. To improve the existing work issues, in this work, we study how to construct compressed database systems that provides better in performance than traditional techniques and to store the database backups to multiple storages. The compression of database systems for real time environment is developed with our proposed Hierarchical Iterative Row-Attribute Compression (HIRAC) algorithm, provides good compression, while allowing access even at attribute level. HIRAC algorithm repeatedly increases the compression ratio at each scan of the database systems. The quantity of compression can be computed based on the number of iterations on the rows. After compressing the real time database systems, it allows database backups to be stored at multiple devices in parallel. Extensive experiments were conducted to estimate the performance of the proposed parallel multi-storage backup compression (PMBC) using HIRAC algorithm with respect to previously known tehniques, such as attribute-level compression methods